David Lee
A hybrid approach to distributed constraint satisfaction.
Authors
Dr Ines Arana i.arana@rgu.ac.uk
Academic Strategic Lead
Dr Hatem Ahriz h.ahriz@rgu.ac.uk
Principal Lecturer
Dr Kit-ying Hui k.hui@rgu.ac.uk
Lecturer
Contributors
Daniel Dochev
Editor
Marco Pistore
Editor
Paolo Traverso
Editor
Abstract
We present a hybrid approach to Distributed Constraint Satisfaction which combines incomplete, fast, penalty-based local search with complete, slower systematic search. Thus, we propose the hybrid algorithm PenDHyb where the distributed local search algorithm DisPeL is run for a very small amount of time in order to learn about the difficult areas of the problem from the penalty counts imposed during its problem-solving. This knowledge is then used to guide the systematic search algorithm SynCBJ. Extensive empirical results in several problem classes indicate that PenDHyb is effective for large problems.
Citation
LEE, D., ARANA, I., AHRIZ, H. and HUI, K.-Y. 2008. A hybrid approach to distributed constraint satisfaction. In Dochev, D., Pistore, M. and Traverso, P. (eds.) Proceedings of the 13th International conference on artificial intelligence: methodology, systems and applications (AIMSA 2008), 4-6 September 2008, Varna, Bulgaria. Lecture notes in computer science, 5253. Berlin: Springer [online], pages 375-379. Available from: https://doi.org/10.1007/978-3-540-85776-1_33
Conference Name | 13th International conference on artificial intelligence: methodology, systems and applications (AIMSA 2008) |
---|---|
Conference Location | Varna, Bulgaria |
Start Date | Sep 4, 2008 |
End Date | Sep 6, 2008 |
Acceptance Date | Dec 31, 2008 |
Online Publication Date | Dec 31, 2008 |
Publication Date | Dec 31, 2008 |
Deposit Date | Dec 23, 2008 |
Publicly Available Date | Dec 23, 2008 |
Publisher | Springer |
Pages | 375-379 |
Series Title | Lecture notes in computer science |
Series Number | 5253 |
ISBN | 3540857753; 9783540857754 |
DOI | https://doi.org/10.1007/978-3-540-85776-1_33 |
Keywords | Constraint satisfaction; Distributed AI; Hybrid systems |
Public URL | http://hdl.handle.net/10059/283 |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
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